mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
Bias updates bug fix
This commit is contained in:
parent
809f924db2
commit
153705226d
5
Makefile
5
Makefile
@ -1,5 +1,6 @@
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GPU=1
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DEBUG=0
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ARCH= -arch=sm_35
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VPATH=./src/
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EXEC=cnn
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@ -8,7 +9,6 @@ OBJDIR=./obj/
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CC=gcc
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NVCC=nvcc
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OPTS=-O3
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LINKER=$(CC)
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LDFLAGS=`pkg-config --libs opencv` -lm -pthread
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COMMON=`pkg-config --cflags opencv` -I/usr/local/cuda/include/
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CFLAGS=-Wall -Wfatal-errors
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@ -20,7 +20,6 @@ CFLAGS+=-O0 -g
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endif
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ifeq ($(GPU), 1)
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LINKER=$(NVCC)
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COMMON+=-DGPU
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CFLAGS+=-DGPU
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LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas
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@ -43,7 +42,7 @@ $(OBJDIR)%.o: %.c
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$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
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$(OBJDIR)%.o: %.cu
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$(NVCC) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
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$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
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.PHONY: clean
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33
src/cnn.c
33
src/cnn.c
@ -212,7 +212,8 @@ void train_imagenet(char *cfgfile)
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//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
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srand(time(0));
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network net = parse_network_cfg(cfgfile);
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set_learning_network(&net, net.learning_rate, net.momentum, net.decay);
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//test_learn_bias(*(convolutional_layer *)net.layers[1]);
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//set_learning_network(&net, net.learning_rate, 0, net.decay);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 3072;
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int i = net.seen/imgs;
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@ -383,25 +384,26 @@ void test_visualize(char *filename)
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cvWaitKey(0);
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}
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void test_cifar10()
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void test_cifar10(char *cfgfile)
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{
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network net = parse_network_cfg("cfg/cifar10_part5.cfg");
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network net = parse_network_cfg(cfgfile);
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data test = load_cifar10_data("data/cifar10/test_batch.bin");
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clock_t start = clock(), end;
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float test_acc = network_accuracy(net, test);
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float test_acc = network_accuracy_multi(net, test, 10);
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end = clock();
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printf("%f in %f Sec\n", test_acc, (float)(end-start)/CLOCKS_PER_SEC);
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visualize_network(net);
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cvWaitKey(0);
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printf("%f in %f Sec\n", test_acc, sec(end-start));
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//visualize_network(net);
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//cvWaitKey(0);
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}
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void train_cifar10()
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void train_cifar10(char *cfgfile)
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{
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srand(555555);
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network net = parse_network_cfg("cfg/cifar10.cfg");
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srand(time(0));
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network net = parse_network_cfg(cfgfile);
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data test = load_cifar10_data("data/cifar10/test_batch.bin");
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int count = 0;
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int iters = 10000/net.batch;
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int iters = 50000/net.batch;
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data train = load_all_cifar10();
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while(++count <= 10000){
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clock_t time = clock();
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@ -410,9 +412,9 @@ void train_cifar10()
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if(count%10 == 0){
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float test_acc = network_accuracy(net, test);
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printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,sec(clock()-time));
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//char buff[256];
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//sprintf(buff, "unikitty/cifar10_%d.cfg", count);
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//save_network(net, buff);
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/cifar10_%d.cfg", count);
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save_network(net, buff);
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}else{
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printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, sec(clock()-time));
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}
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@ -709,8 +711,7 @@ int main(int argc, char **argv)
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}
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#endif
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if(0==strcmp(argv[1], "cifar")) train_cifar10();
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else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
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if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
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else if(0==strcmp(argv[1], "test_correct_nist")) test_correct_nist();
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else if(0==strcmp(argv[1], "test")) test_imagenet();
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//else if(0==strcmp(argv[1], "server")) run_server();
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@ -724,7 +725,9 @@ int main(int argc, char **argv)
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return 0;
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}
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else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2]);
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else if(0==strcmp(argv[1], "ctrain")) train_cifar10(argv[2]);
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else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
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else if(0==strcmp(argv[1], "ctest")) test_cifar10(argv[2]);
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else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2]);
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//else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
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else if(0==strcmp(argv[1], "detect")) test_detection(argv[2]);
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@ -78,8 +78,6 @@ void secret_update_connected_layer(connected_layer *layer)
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axpy_cpu(layer->outputs, 1, layer->bias_updates, 1, layer->bias_prev, 1);
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scal_cpu(layer->outputs, 0, layer->bias_updates, 1);
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//printf("rate: %f\n", layer->learning_rate);
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axpy_cpu(layer->outputs, layer->learning_rate, layer->bias_prev, 1, layer->biases, 1);
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axpy_cpu(layer->inputs*layer->outputs, -layer->decay, layer->weights, 1, layer->weight_prev, 1);
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@ -32,7 +32,7 @@ __global__ void learn_bias(int batch, int n, int size, float *delta, float *bias
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{
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__shared__ float part[BLOCK];
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int i,b;
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int filter = (blockIdx.x + blockIdx.y*gridDim.x);
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int filter = blockIdx.x;
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int p = threadIdx.x;
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float sum = 0;
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for(b = 0; b < batch; ++b){
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@ -52,8 +52,7 @@ extern "C" void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
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{
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int size = convolutional_out_height(layer)*convolutional_out_width(layer);
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learn_bias<<<cuda_gridsize(layer.n), BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu);
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learn_bias<<<layer.n, BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu);
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check_error(cudaPeekAtLastError());
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}
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@ -96,9 +95,6 @@ extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, float
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gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
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}
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activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
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cuda_pull_array(layer.output_gpu, layer.output, m*n*layer.batch);
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//for(i = 0; i < m*n*layer.batch; ++i) printf("%f, ", layer.output[i]);
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//printf("\n");
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}
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extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, float *in, float *delta_gpu)
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@ -153,6 +149,16 @@ extern "C" void push_convolutional_layer(convolutional_layer layer)
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extern "C" void update_convolutional_layer_gpu(convolutional_layer layer)
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{
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int size = layer.size*layer.size*layer.c*layer.n;
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/*
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cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
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cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
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cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, size);
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cuda_pull_array(layer.filters_gpu, layer.filters, size);
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printf("Bias: %f updates: %f\n", mse_array(layer.biases, layer.n), mse_array(layer.bias_updates, layer.n));
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printf("Filter: %f updates: %f\n", mse_array(layer.filters, layer.n), mse_array(layer.filter_updates, layer.n));
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*/
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axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
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scal_ongpu(layer.n,layer.momentum, layer.bias_updates_gpu, 1);
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@ -239,7 +239,8 @@ void *load_in_thread(void *ptr)
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{
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struct load_args a = *(struct load_args*)ptr;
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*a.d = load_data(a.paths, a.n, a.m, a.labels, a.k, a.h, a.w);
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normalize_data_rows(*a.d);
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translate_data_rows(*a.d, -144);
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scale_data_rows(*a.d, 1./128);
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free(ptr);
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return 0;
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}
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@ -42,8 +42,6 @@ char *get_layer_string(LAYER_TYPE a)
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return "none";
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}
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network make_network(int n, int batch)
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{
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network net;
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@ -61,7 +59,6 @@ network make_network(int n, int batch)
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return net;
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}
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void forward_network(network net, float *input, float *truth, int train)
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{
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int i;
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@ -176,6 +176,7 @@ float * get_network_delta_gpu_layer(network net, int i)
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float train_network_datum_gpu(network net, float *x, float *y)
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{
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//clock_t time = clock();
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int x_size = get_network_input_size(net)*net.batch;
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int y_size = get_network_output_size(net)*net.batch;
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if(!*net.input_gpu){
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@ -185,10 +186,18 @@ float train_network_datum_gpu(network net, float *x, float *y)
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cuda_push_array(*net.input_gpu, x, x_size);
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cuda_push_array(*net.truth_gpu, y, y_size);
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}
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//printf("trans %f\n", sec(clock() - time));
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//time = clock();
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forward_network_gpu(net, *net.input_gpu, *net.truth_gpu, 1);
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//printf("forw %f\n", sec(clock() - time));
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//time = clock();
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backward_network_gpu(net, *net.input_gpu);
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//printf("back %f\n", sec(clock() - time));
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//time = clock();
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update_network_gpu(net);
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float error = get_network_cost(net);
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//printf("updt %f\n", sec(clock() - time));
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//time = clock();
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return error;
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}
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@ -233,6 +233,14 @@ float constrain(float a, float max)
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return a;
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}
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float mse_array(float *a, int n)
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{
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int i;
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float sum = 0;
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for(i = 0; i < n; ++i) sum += a[i]*a[i];
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return sqrt(sum/n);
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}
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void normalize_array(float *a, int n)
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{
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int i;
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@ -22,6 +22,7 @@ void scale_array(float *a, int n, float s);
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void translate_array(float *a, int n, float s);
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int max_index(float *a, int n);
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float constrain(float a, float max);
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float mse_array(float *a, int n);
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float rand_normal();
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float rand_uniform();
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float sum_array(float *a, int n);
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